fitModel {ccems}R Documentation

Fit Model

Description

This function fits a model/hypothesis created by mkModel. It is typically passed to lapply or clusterApplyLB to fit a list of model objects, typically within ems.

Usage

 fitModel(model) 

Arguments

model The output list of mkModel.

Details

The main output of this function is the report component of its value (see below) which is also echoed to the screen during computations.

Value

The input argument model extended to include the following fields:

echk A matrix that checks the TCC solver and model$fback. Matrix column names that end in Q should match their sans-Q counterparts.
eSS The expected steady state concentrations of complexes and free reactants. For each row of the data dataframe there is a row in this matrix. Its contents are the TCC solver's expected free reactant concentrations and the result of applying model$fback to them to create expected complex concentrations.
res The residuals of the fit.
nData The number of data points/rows in the data dataframe model$d.
SSE The initial and final sum of squared errors (i.e. residual sum of squares).
AIC The initial and final Akaike Information Criterion values, corrected for small samples. Since nonlinear least squares is used AIC = N*log(SSE/N)+2*P + 2*P*(P+1)/(N-P-1) + N*log(2*pi) + N where N = nData and P is the number of estimated parameters (including the variance).
nOptParams The number of optimized parameters, i.e. the length of the parameter vector sent to optim.
hess This is TRUE if the determinant of the Hessian of the log-likelihood evaluated at the optimum is greater than zero, i.e. if the hessian can be inverted to create a parameter estimate covariance matrix.
CI Confidence intervals. Unlike those in model$report these are numeric rather than strings and these are not expressed as concentrations raised to integer powers (in cases of complete dissociation constants).
cpu The amount of computing time (in minutes) taken to fit the model.
report An extension of model$params to include parameter point estimates and confidence intervals (see CI above). The final column holds numerics and the pointEstimate column holds strings of the same numbers expressed as powers in cases of complete dissociation constants.

Note

This work was supported by the National Cancer Institute (K25CA104791).

Author(s)

Tom Radivoyevitch (txr24@case.edu)

References

Radivoyevitch, T. (2008) Equilibrium model selection: dTTP induced R1 dimerization. BMC Systems Biology 2, 15.

See Also

mkModel,ems,ccems

Examples

library(ccems)
topology <- list(  
        heads=c("R1t0","R2t0"),  
        sites=list(       
                s=list(                     # s-site    thread #
                        m=c("R1t1"),        # monomer      1
                        d=c("R2t1","R2t2")  # dimer        2
                )
        )
) 
g <- mkg(topology,TCC=TRUE) 
data(RNR)
d1 <- subset(RNR,(year==2001)&(fg==1)&(G==0)&(t>0),select=c(R,t,m,year))
d2 <- subset(RNR,year==2006,select=c(R,t,m,year)) 
dRt <- rbind(d1,d2)
names(dRt)[1:2] <- paste(strsplit(g$id,split="")[[1]],"T",sep="")#e.g. to form "RT"
rownames(dRt) <- 1:dim(dRt)[1] # lose big number row names of parent dataframe

## Not run: 
models <- list(
       mkModel(g,"IIJJ",dRt,Kjparams=c(R2t0=Inf, R1t1=Inf,R2t1=1,   R2t2=1)), 
       mkModel(g,"IIIJ",dRt,Kjparams=c(R2t0=Inf, R1t1=Inf,R2t1=Inf, R2t2=1))
       )
# the next line fits the list of two models above in series on a single processor 
fmodels <- lapply(models,fitModel) 
## End(Not run)
# Note that fitModel always delivers a summary of the fit to the screen as a byproduct. 
# The output of the call is assigned to avoid scrolling up through the returned large 
# fitted list of models in order to find this summary. 

[Package ccems version 1.02 Index]